Sensors, Journal Year: 2025, Volume and Issue: 25(10), P. 3108 - 3108
Published: May 14, 2025
The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective capturing spatial contextual information, often face challenges related to high costs, privacy concerns, susceptibility environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven identification system that leverages WiFi-based wireless sensing contrastive learning techniques. IdentiFi utilizes self-supervised semi-supervised extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even dynamic, multi-occupant settings. system’s temporal contrasting modules enhance its ability model motion reduce multi-user interference, class-aware minimizes extensive labeled datasets. Extensive evaluations demonstrate outperforms existing methods terms scalability, adaptability, preservation, making it highly suitable homes, healthcare facilities, security systems, personalized services.
Language: Английский